#encoding=utf8
import numpy as np
import pickle
import os
import sys
from tqdm import tqdm
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
import seaborn as sns

def load_dataset(file_name):
    '''
    从文件读入数据集
    被多处调用，请勿删除或改动本函数！！！
    '''
    try:
        with open(file_name, 'rb') as f:
            raw_dataset = pickle.load(f)
    except FileNotFoundError:
        print(f"错误: 文件 {file_name} 未找到。请确保文件路径正确。")
        return None, None
    
    try:
        example_image = raw_dataset[0][0]
    except KeyError:
        print("错误: 数据集格式不正确，无法找到类别0的数据。")
        return None, None
    except TypeError:
        print("错误: 数据集格式不正确，类别0的数据不是列表或数组。")
        return None, None

    dataset = np.empty((0, example_image.size))
    labels = np.empty((0, 1))
    
    total_images = 0
    for i_class in raw_dataset.keys():
        images_list = raw_dataset.get(i_class, [])
        if not isinstance(images_list, list) or len(images_list) == 0:
            continue
        for image in images_list:
            features = image.flatten() / 255.0
            dataset = np.vstack((dataset, features))
            labels = np.vstack((labels, i_class))
            total_images += 1
            
    print(f"成功加载 {total_images} 张手写数字图片。")
    return dataset, labels

class Classifier:
    def __init__(self):
        self.model = None
        self.pipeline = None
        self.train_dataset, self.train_labels = load_dataset('./step1/input/training_dataset.pkl')
        if self.train_dataset is None:
            raise RuntimeError("训练数据集加载失败，无法继续。")

    def train(self):
        print("=== 正在配置随机森林模型并寻找最优参数... ===")

        pipeline = Pipeline([
            ('scaler', StandardScaler()),
            ('rf', RandomForestClassifier(random_state=42, n_jobs=-1))
        ])

        param_grid = {
            'rf__n_estimators': [50, 100, 200],
            'rf__max_depth': [None, 10, 20],
            'rf__min_samples_leaf': [1, 5, 10]
        }

        grid_search = GridSearchCV(
            pipeline,
            param_grid,
            cv=5,
            scoring='accuracy',
            n_jobs=-1,
            verbose=2
        )
        
        train_labels = self.train_labels.ravel()
        grid_search.fit(self.train_dataset, train_labels)

        self.model = grid_search.best_estimator_
        self.pipeline = self.model

        print("=== 模型训练完成 ===")
        print(f"最佳参数: {grid_search.best_params_}")
        print(f"最佳交叉验证准确率: {grid_search.best_score_:.4f}")

        self.generate_results(grid_search)

    def generate_results(self, grid_search):
        output_dir = "./step1/output/rf_grid_search"
        os.makedirs(output_dir, exist_ok=True)
        
        results = grid_search.cv_results_
        
        self.save_to_markdown(results, output_dir)
        
        # 传入 grid_search 对象
        self.generate_heatmap(grid_search, results, output_dir)

    def save_to_markdown(self, results, output_dir):
        result_path = os.path.join(output_dir, "result_grid_search.md")
        with open(result_path, "w", encoding="utf-8") as f:
            f.write("# 随机森林超参数网格搜索结果\n\n")
            f.write("| n_estimators | max_depth | min_samples_leaf | 交叉验证平均准确率 |\n")
            f.write("|--------------|-----------|------------------|----------------------|\n")
            for params, score in zip(results['params'], results['mean_test_score']):
                n_est = params.get('rf__n_estimators', '-')
                max_dep = params.get('rf__max_depth', '-')
                min_leaf = params.get('rf__min_samples_leaf', '-')
                f.write(f"| {n_est} | {max_dep} | {min_leaf} | {score:.4f} |\n")
        print(f"超参数搜索结果已保存到 {result_path}")

    def generate_heatmap(self, grid_search, results, output_dir):
        print("\n=== 正在生成超参数准确率热力图... ===")
        
        scores = results['mean_test_score']
        
        param_grid = grid_search.param_grid
        
        n_estimators_values = param_grid['rf__n_estimators']
        max_depth_values = param_grid['rf__max_depth']
        
        scores_matrix = np.zeros((len(max_depth_values), len(n_estimators_values)))
        
        for i, max_depth in enumerate(max_depth_values):
            for j, n_estimators in enumerate(n_estimators_values):
                match_index = np.where(
                    (results['param_rf__n_estimators'].data == n_estimators) &
                    (results['param_rf__max_depth'].data == max_depth) &
                    (results['param_rf__min_samples_leaf'].data == 1)
                )[0]
                
                if len(match_index) > 0:
                    scores_matrix[i, j] = results['mean_test_score'][match_index[0]]
        
        plt.rcParams['font.sans-serif'] = ['SimHei']
        plt.rcParams['axes.unicode_minus'] = False
        plt.figure(figsize=(10, 8))
        
        ax = sns.heatmap(
            scores_matrix * 100,
            annot=True,
            fmt=".2f",
            cmap="RdYlGn",
            xticklabels=n_estimators_values,
            yticklabels=[str(x) if x is not None else '无限制' for x in max_depth_values],
            vmin=scores.min() * 100,
            vmax=scores.max() * 100
        )
        
        ax.set_title('随机森林超参数准确率热力图 (min_samples_leaf=1)', fontsize=16)
        ax.set_xlabel('树的数量 (n_estimators)', fontsize=12)
        ax.set_ylabel('树的最大深度 (max_depth)', fontsize=12)
        plt.tight_layout()
        
        figure_path = os.path.join(output_dir, 'rf_heatmap.png')
        plt.savefig(figure_path)
        plt.close()
        print(f"超参数热力图已保存为 '{figure_path}'")

    def predict(self, test_dataset):
        predicted_labels = self.pipeline.predict(test_dataset)
        return predicted_labels

def calculate_accuracy(file_name, classifier):
    test_dataset, test_labels = load_dataset(file_name)
    if test_dataset is None:
        return 0
    random_indices = np.random.permutation(test_dataset.shape[0])
    test_dataset = test_dataset[random_indices,:]
    test_labels = test_labels[random_indices,:]
    predicted_labels = classifier.predict(test_dataset)
    if isinstance(predicted_labels, np.ndarray):
        if predicted_labels.size != test_labels.size:
            print('错误：输出的标签数量与测试集大小不一致')
            accuracy = 0
        else:
            accuracy = np.mean(predicted_labels.flatten()==test_labels.flatten())
    else:
        print('错误：输出格式有误，必须为ndarray格式')
        accuracy = 0
    return accuracy

if __name__ == '__main__':
    classifier = Classifier()
    classifier.train()

    sum_accuracies = 0
    num_test_datasets = 0

    test_dir = './step1/input'
    
    test_files = ['test_dataset_clean.pkl'] + [
        f'test_dataset_noise_type{noise}_level{level}.pkl'
        for noise in range(1, 7)
        for level in range(1, 4)
    ]

    print("\n=== 正在对所有测试集进行评估... ===")
    with tqdm(total=len(test_files), desc="正在测试", file=sys.stdout) as pbar:
        for file_name in test_files:
            file_path = os.path.join(test_dir, file_name)
            
            pbar.set_description(f"正在测试: {file_name}")
            accuracy = calculate_accuracy(file_path, classifier)
            pbar.set_postfix({'正确率': f'{accuracy:.4f}'})
            pbar.update(1)

            sum_accuracies += accuracy
            num_test_datasets += 1
    
    mean_accuracies = sum_accuracies / num_test_datasets
    print(f'\n你在总共{num_test_datasets}个测试集上的平均正确率为：{mean_accuracies:.4f}')